Speech enhancement comprises the improvement of intelligibility and quality of speech contaminated by noise, typically in an additive form [1]}. It is thus broadly applicable to scenarios that need to palliate masking artifacts over the speech, like communication systems, hearing aids, or cochlear implants, where enhancing the signal prior to amplification significantly reduces discomfort and increases intelligibility [2]}. Deep learning has been broadly applied to this field, either in the form of regression of waveform samples or clean spectral components, as well as prediction of masking elements over spectral bins [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}.
A recent trend in speech enhancement is also the use of generative adversarial networks (GANs) [10]}, with its first application being the speech enhancement GAN (SEGAN) [11]}. Other GAN variations have been used to avoid using aligned clean/noisy corpora [12]} or using other adversarial losses and/or domains [13]}, [14]}. SEGAN has also been applied to speech regeneration as a whispered-to-voiced alaryngeal speech conversion [15]}, thus extending the previous denoising approach.
